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380113

Arabic Sentiment Analysis using Deep Learning and Machine Learning approaches.

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Last updated: 03 Jan 2025

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Abstract

Sentiment analysis is defined as an analysis of text to determine the sentiment expressed within it. This text emphasizes the significance of sentiment analysis in web mining and data classification, with detailed illustrations on sentiment analysis of the Arabic language. This study proposed a sentiment analysis framework to review the Arabic text. Two textual representations were explored: term frequency-inverse document frequency (TF-IDF) and word embedding via Word2vec. Various methods have been suggested for categorizing sentiments in Arabic text based on a dependable dataset, including Long Short-Term Memory (LSTM), hybrid LSTM-CNN, Convolutional Neural Network (CNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), and Random Forest (RF). The findings indicated that these methods enhanced Accuracy, precision, Recall, and F1-score. The LR and SVM classifiers accomplished the highest Accuracy with 87%, while the other classifiers (LSTM), (CNN-LSTM), (CNN), (MNB), (RF), and (DT) achieved accuracies with 86.41%, 86.10%, 85.26%, 85%, 84% and 81% respectively.

DOI

10.21608/jocc.2024.380113

Keywords

Sentiment Analysis, Sentiment Classification, Convolutional Neural Networks, Long-short Term Memory networks, Logistic Regression Random Forest

Authors

First Name

gawaher

Last Name

hussein

MiddleName

soliman

Affiliation

Information systems department , faculty of computers and informatics, zagazig university, cairo,Egypt

Email

gawaherahmed@yahoo.com

City

cairo

Orcid

-

First Name

Abdelnasser

Last Name

riad

MiddleName

-

Affiliation

aculty of Computer Science, Misr International University Cairo, Egypt

Email

abdelnasser.riad@miuegypt.edu.eg

City

-

Orcid

-

Volume

3

Article Issue

2

Related Issue

50382

Issue Date

2024-07-01

Receive Date

2024-02-05

Publish Date

2024-07-31

Page Start

10

Page End

22

Online ISSN

2636-3577

Article File

JOCC_Volume 3_Issue 2_Pages 10-22.pdf

PDF . 1.6MB

Link

https://jocc.journals.ekb.eg/article_380113.html

Detail API

https://jocc.journals.ekb.eg/service?article_code=380113

Order

2

Type

Original Article

Type Code

731

Publication Type

Journal

Publication Title

Journal of Computing and Communication

Publication Link

https://jocc.journals.ekb.eg/

MainTitle

Arabic Sentiment Analysis using Deep Learning and Machine Learning approaches.

Details

Type

Article

Created At

24 Dec 2024